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collaborative project that spans multiple continents. You will contribute to the development of new chronobiological analytics on existing data, design experiments to collect novel chronobiological data, engage
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(diploma, master's degree) in transport engineering or civil/electrical/control engineering or mathematics, or related study programs with a solid basis in optimization Description of the PhD topic
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description
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, localization, and sensing, with a focus on developing next-generation multiple-antenna systems while optimizing overall system performance. As a doctoral student, you devote most of your time to doctoral studies
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, investigate error mitigation techniques Cooperate and actively work with international collaborators Your Profile: Master’s degree in physics, mathematics, or computer science Strong interest in both developing
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with high-dimensional, often noisy, data sets; and mathematical modelling approaches that reduce the dimensionality of parameter spaces and produce mechanistically realistic, experimentally testable
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mathematical integration, with the desired outcome being to increase the water and/or nitrogen use efficiency of Australian cropping systems. We propose to use a range of emerging data science approaches within
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The Max Planck Institute for Neurobiology of Behavior – caesar • | Bonn, Nordrhein Westfalen | Germany | about 23 hours ago
(or equivalent) from any relevant field (life sciences, mathematics, physics, computer science, engineering, etc.) to be immersed in a stimulating environment that provides novel technologies to elucidate
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Attributes and Behaviour Ability to negotiate and prioritise multiple, competing responsibilities and to work to deadlines Be open to new ideas, and welcome different perspectives and new ways of thinking
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Engineering Research Group) Aim: Develop a mathematical model for analysing reliability and efficiency of medical processes Background Adverse events and preventable failures in healthcare services represent a